结构化凸优化问题的误差界及一阶算法的收敛速度分析
Error Bounds for Structured Convex Optimization Problems and Convergence Rate Analysis of First-Order Methods
苏文藻   Anthony Man-Cho So
报告人照片   Anthony Man-Cho So received his MSc and PhD degrees in Computer Science with a PhD minor in Mathematics from Stanford University. Dr. So joined The Chinese University of Hong Kong (CUHK) in 2007. He currently serves as Assistant Dean of the Faculty of Engineering and is an Associate Professor in the Department of Systems Engineering and Engineering Management.
  In recent years, we have witnessed a widespread use of first-order methods (FOMs) to solve large-scale structured convex optimization problems. One fundamental issue concerning FOMs is their convergence properties. In this talk, we will present a framework for analyzing the convergence rates of FOMs. A key component of this framework is a so-called error bound condition, which provides a tractable bound on the distance from any candidate solution to the optimal solution set of the problem at hand. We will show that many structured convex optimization problems that arise in practice satisfy the error bound condition. Consequently, we are able to show that many FOMs have a linear rate of convergence when applied to those problems.
报告时间:2015年10月20日14时30分    报告地点:西区科技实验楼西楼1213会议室
报名截止日期:2015年10月19日    可选人数:40